Motion Segmentation

Latest papers

13 papers3 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

FLoTS-SLAM: Optical Flow-Derived Uncertainty for Monocular Dynamic SLAM with Two-Stage Optimization
Oct 16, 2026Haorui Du, Lipu ZhouDynamic SLAM Visual SLAM
The authors are with the School of Instrument Science and Optoelectronics Engineering, Beihang University, Beijing 100191, China
All-St3R SLAM: Real-time RGB Dynamic SLAM with Pixel-wise Motion Masking
Oct 12, 2026Yewon Kim, Paul Hongsuck Seo, Sang Chul AhnDynamic SLAM Motion Segmentation
Center for Artificial Intelligence Research, Korea Institute of Science and Technology, Seoul, Korea (South) · Dept. of Computer Science and Engineering, Korea University, Seoul, Korea (South) · Center for Artificial Intelligence Research, Korea Institute of Science and Technology, and with AI-Robotics, KIST School (UST), Seoul, Korea (South)
Surgical Kinematics from Monocular Video with Learned Articulated Motion Constraints
Sep 23, 2026Mehmet Kerem Turkcan, Soham Samal, Zoran KosticSurgical Robotics Robot Kinematics
Columbia University, New York, NY, USA
Segment Any Motion with Radar: Robust Multimodal Moving-Object Segmentation and Tracking
Sep 8, 2026Jue Wang, Xuan Wang, Hao Zhou +6Camera-Radar Fusion Video Object Segmentation
Harbin Institute of Technology, Shenzhen, China · Great Bay University, Dongguan, China · Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
Motion Cues from Image-based Point Tracking for LiDAR Scene Flow Estimation
May 16, 2026Youngdong Jang, Gyeongrok Oh, Jong Wook Kim +6Point Tracking Motion Segmentation
Korea University · Purdue University · KAIST +1
WildPose: A Unified Framework for Robust Pose Estimation in the Wild
May 12, 2026Jianhao Zheng, Liyuan Zhu, Zihan Zhu +1Camera Pose Estimation Motion Segmentation
1Stanford University · 2ETH Zürich
GenMatter: Perceiving Physical Objects with Generative Matter Models
Apr 24, 2026Eric Li, Arijit Dasgupta, Yoni Friedman +5Video Object Segmentation Motion Segmentation
1MIT CSAIL · 2MIT BCS
FreeArtGS: Articulated Gaussian Splatting Under Free-moving Scenario
Mar 23, 2026Hang Dai, Hongwei Fan, Han Zhang +3Motion Segmentation Articulated Object Reconstruction
CFCS, School of Computer Science, Peking University · PrimeBot · College of Computer Science and Technology, Zhejiang University
RayMap3R: Inference-Time RayMap for Dynamic 3D Reconstruction
Mar 21, 2026Feiran Wang, Zezhou Shang, Gaowen Liu +1Motion Segmentation Dynamic Scene Reconstruction
University of Illinois Chicago · Cisco Research
Robust Dynamic Object Detection in Cluttered Indoor Scenes via Learned Spatiotemporal Cues
Mar 16, 2026Juan Rached, Yixuan Jia, Kota Kondo +1Occupancy Grid Mapping Motion Segmentation
Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
MotionBits: Video Segmentation through Motion-Level Analysis of Rigid Bodies
Mar 6, 2026Howard H. Qian, Kejia Ren, Yu Xiang +2Motion Segmentation Video Object Segmentation
Department of Computer Science Rice University · Department of Computer Science University of Texas at Dallas
GeoMotion: Rethinking Motion Segmentation via Latent 4D Geometry
Feb 25, 2026Xiankang He, Peile Lin, Ying Cui +3Motion Segmentation
College of Computer Science and Technology, Zhejiang University of Technology · Zhejiang Key Laboratory of Visual Information Intelligent Processing · State Key Lab of CAD & CG, Zhejiang University
Real-time Motion Segmentation with Event-based Normal Flow
Feb 24, 2026Sheng Zhong, Zhongyang Ren, Xiya Zhu +3Motion Segmentation Event-Based Vision
Neuromorphic Automation and Intelligence Lab (NAIL) at School of Artificial Intelligence and Robotics, Hunan University, Changsha, China. · Capital One. · University of Maryland, College Park.

Oct 16, 2026J/K move · Enter open · S save

FLoTS-SLAM: Optical Flow-Derived Uncertainty for Monocular Dynamic SLAM with Two-Stage Optimization

Haorui Du, Lipu Zhou

The authors are with the School of Instrument Science and Optoelectronics Engineering, Beihang University, Beijing 100191, China

Dynamic SLAM Visual SLAM Dynamic Scene Reconstruction Motion Segmentation Optical Flow

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Monocular simultaneous localization and mapping (SLAM) remains challenging in dynamic environments. Real-time dynamic object identification in such environments is a critical problem. Recent approaches estimate per-pixel uncertainty from rendering residuals or multiview feature inconsistency to identify dynamic regions. These methods lack direct motion evidence, and their accuracy may degrade when reconstruction is inaccurate or features are ambiguous. In addition, such uncertainty merely downweights pixels in bundle adjustment (BA), so inaccurate uncertainty estimates can still impair camera pose estimation and 3D reconstruction. Accordingly, we present a monocular RGB SLAM system for dynamic scenes. Specifically, a motion uncertainty estimation method is proposed to derive uncertainty from the residual between optical flow and camera motion induced flow, directly reflecting object motion. The estimation varies with frame alignment accuracy, reducing false detections and yielding accurate pixel-level masks of dynamic regions. To make BA more robust, we estimate camera poses from high confidence pixels. These poses are then used to reproject dynamic pixels during joint refinement of poses and depth, reducing the influence of object motion on both pose and depth updates. Moreover, we provide a quantitative evaluation of dynamic masks and analyze how these masks affect the downstream tracking and reconstruction of dynamic objects. Our approach improves the mean dynamic mask IoU by 35% over a state-of-the-art baseline on DAVIS, and reduces the average ATE by 59% on Bonn and 19% on TUM relative to the SLAM backbone used in our system. The source code will be made publicly available.